Spoken language understanding (SLU) is an emerging field that resides in-between the fields of speech processing and natural language processing. SLU has a vast array of applications in both the areas of natural human-to-machine communication and human-to-human communication. Examples of such applications include various enterprise applications (e.g., automated customer-care centers and meeting summarization) and various consumer applications (e.g., speech summarization, voice search, spoken document retrieval, and more complex voice interaction with mobile and other types of computing devices, automobiles, robots, and smart home environments). Simply put, SLU in today's human-to-machine spoken dialog systems (also known as conversational interaction systems and conversational understanding systems) aims to extract “meaning” from conversational speech. In other words, SLU in today's human-to-machine spoken dialog systems generally attempts to obtain a conceptual representation (e.g., an understanding of the meaning) of naturally spoken language by leveraging various technologies such as signal processing, pattern recognition, machine learning, and artificial intelligence.
In one particular implementation a dialog system, a SLU module receives transcribed speech queries and extracts their semantic information, which can be used for decision making and response generation. As part of this extraction process it is advantageous to know the relations expressed in the query (e.g., “Who played Jake Sully in Avatar” has relations acted by, character name, and movie name). These relations can be used in one example to form queries to databases or knowledge graphs in order to generate an appropriate response.